Video Machine Learning Engineer

San Diego, California, United States - Software and Services

Imagine what you could do here! At Apple, new insights have a way of becoming extraordinary products, services, and customer experiences very quickly. We are seeking a passionate and innovative machine learning engineer to join a team that is shaping the future of video intelligence. Our team develops cutting‑edge machine learning technologies and systems that power intelligent, interactive video experiences across Apple's products — including Photos, Camera, Spotlight, Face Time, Air Play, and TV+.

This is your opportunity to push the boundaries of what's possible in video understanding, processing, and delivery, and to see your work come to life in products used by hundreds of millions of people worldwide.

As a Video Machine Learning Algorithm Engineer on our team, you will be at the forefront of designing, developing, and deploying machine learning solutions that redefine how video is experienced across Apple's ecosystem. You will collaborate with a world‑class group of researchers and engineers to tackle challenging problems in video processing and understanding — from crafting novel neural network architectures to optimizing models for on‑device performance.

Your work will span the full lifecycle of innovation: researching state‑of‑the‑art techniques, prototyping new ideas, training and evaluating models, and shipping production‑quality solutions that delight users. If you thrive at the intersection of deep learning research and real‑world product impact, this role will give you the platform to do the best work of your career.

• Design and develop novel machine learning algorithms and neural network architectures for video processing, compression, understanding, and enhancement.
• Train, evaluate, and iterate on deep learning models using large‑scale datasets to achieve state‑of‑the‑art performance.
• Optimize machine learning models for efficient deployment on Apple hardware, balancing quality, latency, and resource constraints.
• Implement and integrate end‑to‑end machine learning pipelines into Apple's products and frameworks.
• Collaborate cross‑functionally with product, software, and hardware teams to define requirements and deliver cohesive solutions.
• Investigate and prototype emerging research techniques to solve complex video‑related challenges.
• Analyze and benchmark model performance using rigorous metrics, driving continuous improvement in quality and efficiency.
• Document technical designs, experimental results, and best practices to support team knowledge sharing and reproducibility.

• BS in Computer Science, Electrical Engineering, Machine Learning, or a related field
• Strong foundation in deep learning and neural network design, including hands‑on experience building, training, and deploying models.
• Proficiency in one or more deep learning frameworks such as PyTorch or Tensor Flow.
• Experience with computer vision and/or image and video processing techniques.
• Strong programming skills in Python and/or C/C++, with demonstrated ability to debug and solve complex technical problems.

• MS or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field with a focus on video or visual computing.
• Experience with video codec and compression techniques, including familiarity with standards such as H.264, H.265/HEVC, AV1, or VVC.
• Knowledge of ML‑based image and video codecs, including neural compression and learned representations.
• Experience with diffusion models and generative approaches for image and video synthesis or enhancement.
• Familiarity with advanced video quality metrics (e.g., VMAF, LPIPS, DISTS) and perceptual quality evaluation methodologies.
• Experience optimizing and deploying neural networks on edge devices or mobile platforms.
• Published research in top‑tier venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML) in relevant areas.
• Strong communication and collaboration skills, with the ability to work effectively in cross‑functional teams.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides…